Remote Sensing Image Denoising Based On Signal & Noise Characteristic Analysis

Remote sensing technology is a key to conduct the observation towards ground objects, which proclaim its importance in many areas, such as national security, national economy, scientific research, life of individuals, and so forth. Satellite remote sensing systems receive electromagnetic wave radiation to reflect the real feature of ground objects through indirect observation accurately. However, enormous noise exists during image acquisition and delivery due to the complicated electromagnetic environment out side space, along with the numerous circuit systems inside the satellite and the defects of CCD. It is well known that the existence of noise strongly influences the quality of observation results which surely lead great difficulties to the incoming image processing or manual interpretation. Therefore, effective remote sensing image denoising is a very important preprocessing schedule before anyother image processing can be carried out efficiently.During the acquisition of remote sensing images, there are many reasons which result in different kinds of noise. The characteristics of noise caused by different reasons can be quite distinct from each other. The thermal noise caused by electromagnetic interference or temperature changes obeys Gaussian or Poisson distribution. The striping noise caused by line array CCD image input system response inequality lies in certain direction in the image. The impulsive noise introduced by defection and malfunction of imaging system or state alternation of electric switches and relays is a kind of extreme disturbance, which can totally erase part of the image pixels. Different kinds of noise have distinct features and they behave differently in noised images. It is impossible to handle different kinds of noise by any individual algorithm. For example, the wavelet based shrinkage algorithms, which have been proved to be quite efficient in suppressing Gaussian noise, become invalid in front of striping noise or impulsive noise. In this case, we must research each noise characters and mechanisms individually to pursue good denoising results. Moreover, the key factor lies in any successful denoising method is to find the different characteristics between signal and noise. As for remote sensing image, the obvious characteristic is the abundant detail information and 2-D directional information. So far, there were no such specified documents to research the noise suppression method based on synthetic analysis towards the characteristics of different kinds of noise.In this paper, based on the exploration of working mechanism in satellite remote sensing imaging system, the distinct source characteristics between remote sensing image and noise are discussed. Then, according to diverse characteristics of different noise, respective denoising methods are proposed for each one of them. The main work of this dissertation can be summarized as follows:1. Satellite imaging system noise source and character analyzing. This paper introduces, summarizes and probes into the distinct source and characteristics of different kinds of noise imported in imaging acquisition and delivery, including Gaussian noise, striping noise, and impulsive noise.2. Destriping method researching in linear array CCD system. This chapter analyzes striping noise caused by unequal response function of each pixel in linear array CCD sensors. After discussing the characteristic of random striping noise, a 2-D directional filter based de-striping method is proposed. By utilizing the outstanding frequency selecting feature of the narrow channel directional filter, this method divides striping noise into another sub-band aside from image signal and point-wise noise, then use average compensation algorithm in horizontal sub-band to remove striping noise. Experiment results prove that not only can it remove isolated random striping noise, but also can it retain detailed information in CCD image.3. Zero-mean Gaussian noise suppression research. Based on Signal & Noise characteristic analysis in remote satellite images, a Gaussian noise suppression method based on adaptive directional lifting (ADL) is proposed by means of utilizing the rich directional information induced by textures and edges. The proposed method is constructed based on the scheme of traditional lifting scheme. By integrating local direction information in each lifting steps, ADL compresses most image high frequency information induced by edges and textures into low frequency sub-band. This procedure can overcome the drawbacks of traditional wavelet on lacking of direction selecting elasticity. Since ADL can compress most edge and texture information into low frequency sub-band, leaving only point-wise noise in high frequency sub-band, the thresholding processing can effectively remove noise while retain image information.4. Impulsive noise removal method research. Analyze impulsive noise in satellite imaging system, propose a nonlinear down sampling and subsection auto-regressive interpolation based impulsive noise removal algorithm. At the first step, this algorithm constructs a half resolution image without impulsive noise from the noised image by means of non-uniform down-sampling. After that, the piecewise auto-regressive interpolation method is used to restore low resolution image into the original size. The proposed algorithm innovates in introducing down sampling and interpolation concept into impulsive noise removal, which fully utilizes every uncontaminated pixels and two-dimensional structural information wihtin to achieve better denoising results.